PROJECT SUMMARY/ABSTRACT Recent studies suggest high ambient temperatures increase the risk of preterm birth (<37 completed weeks of gestation), a leading cause of infant mortality and long-term neurological disabilities. Infants born early term (37-38 weeks) also have more morbidity compared to full term births. Under climate projections, heat waves are expected to increase in frequency, intensity, and duration, and many will cause increases in ambient air pollutant concentrations. The proposed research seeks to use large existing databases and robust methodological approaches at multiple spatial scales to test the overarching hypothesis that extreme heat events increase the risk of preterm birth and early term birth, with stronger associations hypothesized to be observed following heat events of longer duration and greater intensity. Using national birth record data from the National Center for Health Statistics, we will assess these relationships in 114 large U.S. cities (covering 54% of the population) at a county-level spatial resolution over a 36-year period (1981-2016). We will additionally obtain birth record data from eight populous and geographically representative U.S. states (covering 40% of the population) over the period 1990-2016 to assess relationships at ZIP code or finer resolution and to examine possible mediation of heat wave associations by accompanying changes in air pollution levels. Meteorology will be characterized by integrating hourly data from multiple weather station networks and satellite-resources, harnessing the strengths of each dataset to maximize spatial and temporal coverage and minimize exposure prediction error. Ambient concentrations of 12 pollutants for the eight selected states will be also characterized by combining Community Multiscale Air Quality Model (CMAQ) outputs with monitor measurements. The statistical models for preterm and early term birth will account for seasonal patterns of conception (a possible source of bias in previous studies), and two-stage analyses using Bayesian hierarchical models will be used to combine information across study locations and assess heterogeneity by climate region, timing of the heat event (within season or across decades), maternal characteristics (educational attainment, race/ethnicity), and location-specific attributes (e.g., contextual socioeconomic indicators, air conditioning prevalence, urbanicity). Precise estimation of this heterogeneity is possible due to the exceptionally large sample, which also allows for examination of heat events defined using higher intensity and duration thresholds than previously assessed. The multi-scale approach facilitates assessment and propagation of uncertainty due to exposure prediction errors, spatial aggregation, and residential mobility during pregnancy. Results can be used to inform local public health warning systems such as heat advisories that target pregnant women with the ultimate goal of reducing early birth and its sequelae. The study will also yield lasting benefits for future studies of climate and health through the creation of an integrated, spatial and temporally-resolved, publically-available meteorology dataset for the continental U.S.